Overview

Dataset statistics

Number of variables56
Number of observations32316
Missing cells182786
Missing cells (%)10.1%
Duplicate rows1993
Duplicate rows (%)6.2%
Total size in memory13.8 MiB
Average record size in memory448.0 B

Variable types

NUM24
BOOL18
CAT14

Warnings

Dataset has 1993 (6.2%) duplicate rows Duplicates
SAE Net Horsepower @ RPM is highly correlated with SAE Net Torque @ RPMHigh correlation
SAE Net Torque @ RPM is highly correlated with SAE Net Horsepower @ RPMHigh correlation
Stability Control is highly correlated with Traction ControlHigh correlation
Traction Control is highly correlated with Stability ControlHigh correlation
Turning Diameter - Curb to Curb (ft) is highly correlated with Wheelbase (in)High correlation
Wheelbase (in) is highly correlated with Turning Diameter - Curb to Curb (ft)High correlation
EPA Fuel Economy Est - City (MPG) has 5289 (16.4%) missing values Missing
Engine has 1975 (6.1%) missing values Missing
Drivetrain has 1716 (5.3%) missing values Missing
Base Curb Weight (lbs) has 12860 (39.8%) missing values Missing
Passenger Volume (ft³) has 15560 (48.1%) missing values Missing
Wheelbase (in) has 2015 (6.2%) missing values Missing
Track Width, Front (in) has 12186 (37.7%) missing values Missing
Height, Overall (in) has 16688 (51.6%) missing values Missing
Fuel Tank Capacity, Approx (gal) has 16701 (51.7%) missing values Missing
SAE Net Torque @ RPM has 2067 (6.4%) missing values Missing
Fuel System has 2830 (8.8%) missing values Missing
SAE Net Horsepower @ RPM has 2014 (6.2%) missing values Missing
Displacement has 2147 (6.6%) missing values Missing
Trans Description Cont. has 2037 (6.3%) missing values Missing
Trans Type has 1981 (6.1%) missing values Missing
Basic Miles/km has 2117 (6.6%) missing values Missing
Basic Years has 2117 (6.6%) missing values Missing
Corrosion Miles/km has 3004 (9.3%) missing values Missing
Corrosion Years has 2966 (9.2%) missing values Missing
Drivetrain Miles/km has 2788 (8.6%) missing values Missing
Drivetrain Years has 2788 (8.6%) missing values Missing
Turning Diameter - Curb to Curb (ft) has 3431 (10.6%) missing values Missing
Front Wheel Material has 1983 (6.1%) missing values Missing
Stabilizer Bar Diameter - Front (in) has 21109 (65.3%) missing values Missing
Roadside Assistance Years has 21027 (65.1%) missing values Missing
Roadside Assistance Miles/km has 7023 (21.7%) missing values Missing
Category has 1716 (5.3%) missing values Missing
Front tire aspect ratio has 2012 (6.2%) missing values Missing
Front tire speed ratings/cons.type has 6086 (18.8%) missing values Missing
Front tire rim size has 4499 (13.9%) missing values Missing
Passenger Capacity has 1969 (6.1%) zeros Zeros

Reproduction

Analysis started2020-12-30 03:37:25.540287
Analysis finished2020-12-30 03:39:13.511352
Duration1 minute and 47.97 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

MSRP
Real number (ℝ≥0)

Distinct10228
Distinct (%)31.7%
Missing54
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37707.45943
Minimum6929
Maximum548800
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:13.628775image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6929
5-th percentile15450
Q123140
median30557.5
Q340758.75
95-th percentile82900
Maximum548800
Range541871
Interquartile range (IQR)17618.75

Descriptive statistics

Standard deviation32392.37576
Coefficient of variation (CV)0.8590442383
Kurtosis60.58142461
Mean37707.45943
Median Absolute Deviation (MAD)8457.5
Skewness6.315598043
Sum1216518056
Variance1049266007
MonotocityNot monotonic
2020-12-30T03:39:13.879798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
29995640.2%
 
19995550.2%
 
25995430.1%
 
20995400.1%
 
27995390.1%
 
24495360.1%
 
21995340.1%
 
22995330.1%
 
18995330.1%
 
24995330.1%
 
Other values (10218)3185298.6%
 
(Missing)540.2%
 
ValueCountFrequency (%) 
69291< 0.1%
 
71491< 0.1%
 
73991< 0.1%
 
75991< 0.1%
 
77091< 0.1%
 
ValueCountFrequency (%) 
5488002< 0.1%
 
5355001< 0.1%
 
4976502< 0.1%
 
4924253< 0.1%
 
4920001< 0.1%
 

EPA Fuel Economy Est - City (MPG)
Real number (ℝ≥0)

MISSING

Distinct53
Distinct (%)0.2%
Missing5289
Missing (%)16.4%
Infinite0
Infinite (%)0.0%
Mean19.56820217
Minimum9
Maximum66
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:14.125467image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile13
Q116
median18
Q322
95-th percentile29
Maximum66
Range57
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.589964661
Coefficient of variation (CV)0.2856657251
Kurtosis8.40764124
Mean19.56820217
Median Absolute Deviation (MAD)3
Skewness2.058188746
Sum528869.8
Variance31.24770491
MonotocityNot monotonic
2020-12-30T03:39:14.355798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1725567.9%
 
1624637.6%
 
1524517.6%
 
1823487.3%
 
1920026.2%
 
1419246.0%
 
2018605.8%
 
2117585.4%
 
2217555.4%
 
2411473.5%
 
Other values (43)676320.9%
 
(Missing)528916.4%
 
ValueCountFrequency (%) 
94< 0.1%
 
10450.1%
 
111990.6%
 
124621.4%
 
1310803.3%
 
ValueCountFrequency (%) 
662< 0.1%
 
6110< 0.1%
 
608< 0.1%
 
582< 0.1%
 
578< 0.1%
 

Engine
Categorical

MISSING

Distinct13
Distinct (%)< 0.1%
Missing1975
Missing (%)6.1%
Memory size252.5 KiB
l4
9973 
V8
8611 
V6
8596 
Flat
1206 
I6
 
554
Other values (8)
1401 
ValueCountFrequency (%) 
l4997330.9%
 
V8861126.6%
 
V6859626.6%
 
Flat12063.7%
 
I65541.7%
 
l55301.6%
 
l62630.8%
 
V122080.6%
 
Electric1740.5%
 
l31120.3%
 
Other values (3)1140.4%
 
(Missing)19756.1%
 
2020-12-30T03:39:14.604780image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:14.806020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length2
Mean length2.177775715
Min length2

Drivetrain
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing1716
Missing (%)5.3%
Memory size252.5 KiB
FWD
9167 
RWD
9160 
4WD
6378 
AWD
5895 
ValueCountFrequency (%) 
FWD916728.4%
 
RWD916028.3%
 
4WD637819.7%
 
AWD589518.2%
 
(Missing)17165.3%
 
2020-12-30T03:39:15.022305image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:15.179758image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:15.303306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length3
Min length3

Passenger Capacity
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.762903825
Minimum0
Maximum15
Zeros1969
Zeros (%)6.1%
Memory size252.5 KiB
2020-12-30T03:39:15.484174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median5
Q35
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.82401815
Coefficient of variation (CV)0.3829634646
Kurtosis2.723523667
Mean4.762903825
Median Absolute Deviation (MAD)0
Skewness-0.4357000769
Sum153918
Variance3.327042213
MonotocityNot monotonic
2020-12-30T03:39:15.685972image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
51673151.8%
 
6385211.9%
 
429929.3%
 
720106.2%
 
019696.1%
 
316935.2%
 
216045.0%
 
810863.4%
 
122350.7%
 
91380.4%
 
Other values (2)6< 0.1%
 
ValueCountFrequency (%) 
019696.1%
 
11< 0.1%
 
216045.0%
 
316935.2%
 
429929.3%
 
ValueCountFrequency (%) 
155< 0.1%
 
122350.7%
 
91380.4%
 
810863.4%
 
720106.2%
 

Passenger Doors
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
4
21785 
2
8125 
0
 
1716
3
 
690
ValueCountFrequency (%) 
42178567.4%
 
2812525.1%
 
017165.3%
 
36902.1%
 
2020-12-30T03:39:15.909164image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:16.075040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:16.178384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

Base Curb Weight (lbs)
Real number (ℝ≥0)

MISSING

Distinct2208
Distinct (%)11.3%
Missing12860
Missing (%)39.8%
Infinite0
Infinite (%)0.0%
Mean3610.225309
Minimum1808
Maximum8591
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:16.388846image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1808
5-th percentile2560
Q13153
median3497
Q34048
95-th percentile4834
Maximum8591
Range6783
Interquartile range (IQR)895

Descriptive statistics

Standard deviation695.1865738
Coefficient of variation (CV)0.19256044
Kurtosis0.5372050967
Mean3610.225309
Median Absolute Deviation (MAD)436.5
Skewness0.5713473821
Sum70240543.61
Variance483284.3725
MonotocityNot monotonic
2020-12-30T03:39:16.625318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3433970.3%
 
3362880.3%
 
3428870.3%
 
3230820.3%
 
3197670.2%
 
3285670.2%
 
3263660.2%
 
3307640.2%
 
3395630.2%
 
3340630.2%
 
Other values (2198)1871257.9%
 
(Missing)1286039.8%
 
ValueCountFrequency (%) 
180816< 0.1%
 
18472< 0.1%
 
18503< 0.1%
 
185210< 0.1%
 
18562< 0.1%
 
ValueCountFrequency (%) 
85911< 0.1%
 
82321< 0.1%
 
64061< 0.1%
 
62791< 0.1%
 
62751< 0.1%
 

Passenger Volume (ft³)
Real number (ℝ≥0)

MISSING

Distinct435
Distinct (%)2.6%
Missing15560
Missing (%)48.1%
Infinite0
Infinite (%)0.0%
Mean100.4615224
Minimum21.28
Maximum188.4
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:16.874631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum21.28
5-th percentile75.5
Q190.8
median97.5
Q3104
95-th percentile152.9
Maximum188.4
Range167.12
Interquartile range (IQR)13.2

Descriptive statistics

Standard deviation21.93941709
Coefficient of variation (CV)0.2183862693
Kurtosis2.781327495
Mean100.4615224
Median Absolute Deviation (MAD)6.6
Skewness1.089519113
Sum1683333.27
Variance481.3380223
MonotocityNot monotonic
2020-12-30T03:39:17.105909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
933321.0%
 
105.42500.8%
 
93.52360.7%
 
1002270.7%
 
882130.7%
 
522090.6%
 
1021960.6%
 
941940.6%
 
97.51880.6%
 
911750.5%
 
Other values (425)1453645.0%
 
(Missing)1556048.1%
 
ValueCountFrequency (%) 
21.281< 0.1%
 
42.45< 0.1%
 
442< 0.1%
 
45.4410.1%
 
46620.2%
 
ValueCountFrequency (%) 
188.4240.1%
 
177.4360.1%
 
176.910< 0.1%
 
176.62< 0.1%
 
176.58< 0.1%
 

Wheelbase (in)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct365
Distinct (%)1.2%
Missing2015
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean117.8425313
Minimum73.5
Maximum178
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:17.351226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum73.5
5-th percentile98.7
Q1105.1
median111.5
Q3126
95-th percentile157.05
Maximum178
Range104.5
Interquartile range (IQR)20.9

Descriptive statistics

Standard deviation18.06417853
Coefficient of variation (CV)0.1532908224
Kurtosis0.4523704079
Mean117.8425313
Median Absolute Deviation (MAD)8
Skewness1.08601894
Sum3570746.54
Variance326.3145461
MonotocityNot monotonic
2020-12-30T03:39:17.602345image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106.311993.7%
 
143.56752.1%
 
104.36291.9%
 
109.35551.7%
 
105.15201.6%
 
103.14861.5%
 
1164601.4%
 
112.24391.4%
 
1304351.3%
 
109.83961.2%
 
Other values (355)2450775.8%
 
(Missing)20156.2%
 
ValueCountFrequency (%) 
73.5370.1%
 
73.714< 0.1%
 
86.614< 0.1%
 
89.2620.2%
 
89.4200.1%
 
ValueCountFrequency (%) 
17812< 0.1%
 
17610< 0.1%
 
172.41250.4%
 
172270.1%
 
170.3540.2%
 

Track Width, Front (in)
Real number (ℝ≥0)

MISSING

Distinct142
Distinct (%)0.7%
Missing12186
Missing (%)37.7%
Infinite0
Infinite (%)0.0%
Mean61.68542345
Minimum50
Maximum69.4
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:17.815663image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile57.7
Q160
median61.6
Q363.2
95-th percentile66.9
Maximum69.4
Range19.4
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation2.627704767
Coefficient of variation (CV)0.04259847173
Kurtosis0.3853343977
Mean61.68542345
Median Absolute Deviation (MAD)1.6
Skewness0.1684791494
Sum1241727.574
Variance6.904832342
MonotocityNot monotonic
2020-12-30T03:39:18.028374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60.26472.0%
 
60.65911.8%
 
625731.8%
 
62.44801.5%
 
60.74771.5%
 
61.64711.5%
 
614661.4%
 
62.14591.4%
 
634401.4%
 
62.24231.3%
 
Other values (132)1510346.7%
 
(Missing)1218637.7%
 
ValueCountFrequency (%) 
504< 0.1%
 
50.5290.1%
 
50.514< 0.1%
 
54.57< 0.1%
 
55.3240.1%
 
ValueCountFrequency (%) 
69.42< 0.1%
 
68.76< 0.1%
 
68.511< 0.1%
 
68.314< 0.1%
 
68.26< 0.1%
 

Height, Overall (in)
Real number (ℝ≥0)

MISSING

Distinct342
Distinct (%)2.2%
Missing16688
Missing (%)51.6%
Infinite0
Infinite (%)0.0%
Mean66.44527316
Minimum44.3
Maximum110.1
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:18.271384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum44.3
5-th percentile53.9
Q157.3
median67.5
Q374.4
95-th percentile78.5
Maximum110.1
Range65.8
Interquartile range (IQR)17.1

Descriptive statistics

Standard deviation9.372474316
Coefficient of variation (CV)0.1410555465
Kurtosis-0.256598927
Mean66.44527316
Median Absolute Deviation (MAD)9
Skewness0.1833552705
Sum1038406.729
Variance87.84327481
MonotocityNot monotonic
2020-12-30T03:39:18.504197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
76.24011.2%
 
56.53181.0%
 
56.22640.8%
 
73.72620.8%
 
73.82390.7%
 
76.92240.7%
 
76.82120.7%
 
58.12100.6%
 
66.32080.6%
 
56.32050.6%
 
Other values (332)1308540.5%
 
(Missing)1668851.6%
 
ValueCountFrequency (%) 
44.32< 0.1%
 
46.1280.1%
 
46.31< 0.1%
 
46.68< 0.1%
 
47.31< 0.1%
 
ValueCountFrequency (%) 
110.18< 0.1%
 
109.416< 0.1%
 
108.64< 0.1%
 
107.74< 0.1%
 
100.88< 0.1%
 

Fuel Tank Capacity, Approx (gal)
Real number (ℝ≥0)

MISSING

Distinct130
Distinct (%)0.8%
Missing16701
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean22.02699673
Minimum1.9
Maximum48
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:18.743962image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile13.2
Q116.6
median19.5
Q326
95-th percentile36
Maximum48
Range46.1
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation7.017269308
Coefficient of variation (CV)0.3185758546
Kurtosis-0.3739954961
Mean22.02699673
Median Absolute Deviation (MAD)4.2
Skewness0.6855076768
Sum343951.554
Variance49.24206854
MonotocityNot monotonic
2020-12-30T03:39:19.019336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2618195.6%
 
18.58082.5%
 
347882.4%
 
197882.4%
 
13.26201.9%
 
365581.7%
 
185441.7%
 
315431.7%
 
19.54531.4%
 
214391.4%
 
Other values (120)825525.5%
 
(Missing)1670151.7%
 
ValueCountFrequency (%) 
1.93< 0.1%
 
2.34< 0.1%
 
74< 0.1%
 
7.82< 0.1%
 
89< 0.1%
 
ValueCountFrequency (%) 
4810< 0.1%
 
4216< 0.1%
 
39660.2%
 
38.5200.1%
 
382310.7%
 

SAE Net Torque @ RPM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct374
Distinct (%)1.2%
Missing2067
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean269.1232107
Minimum65
Maximum935
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:19.259973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile127
Q1181
median260
Q3335
95-th percentile440
Maximum935
Range870
Interquartile range (IQR)154

Descriptive statistics

Standard deviation102.7460548
Coefficient of variation (CV)0.3817807262
Kurtosis1.027629097
Mean269.1232107
Median Absolute Deviation (MAD)76
Skewness0.7568120606
Sum8140708
Variance10556.75178
MonotocityNot monotonic
2020-12-30T03:39:19.513207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
26010373.2%
 
2587772.4%
 
3807322.3%
 
2075521.7%
 
2955421.7%
 
3655371.7%
 
3604711.5%
 
3054351.3%
 
3254131.3%
 
4004061.3%
 
Other values (364)2434775.3%
 
(Missing)20676.4%
 
ValueCountFrequency (%) 
653< 0.1%
 
66170.1%
 
68280.1%
 
74260.1%
 
82410.1%
 
ValueCountFrequency (%) 
9352< 0.1%
 
8113< 0.1%
 
7925< 0.1%
 
7529< 0.1%
 
7501< 0.1%
 

Fuel System
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing2830
Missing (%)8.8%
Memory size252.5 KiB
SFI
12564 
DI
10644 
Electric FI
6278 
ValueCountFrequency (%) 
SFI1256438.9%
 
DI1064432.9%
 
Electric FI627819.4%
 
(Missing)28308.8%
 
2020-12-30T03:39:19.739918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:19.938107image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:20.033925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length11
Median length3
Mean length4.224780295
Min length2

SAE Net Horsepower @ RPM
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct387
Distinct (%)1.3%
Missing2014
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean258.4166062
Minimum40
Maximum808
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:20.269472image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile130
Q1180
median252
Q3310
95-th percentile424
Maximum808
Range768
Interquartile range (IQR)130

Descriptive statistics

Standard deviation98.6465932
Coefficient of variation (CV)0.3817347293
Kurtosis1.469595956
Mean258.4166062
Median Absolute Deviation (MAD)65
Skewness0.9147305465
Sum7830540
Variance9731.15035
MonotocityNot monotonic
2020-12-30T03:39:20.519618image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
30012513.9%
 
1709372.9%
 
2008222.5%
 
3607282.3%
 
2855621.7%
 
2405391.7%
 
3055141.6%
 
3104831.5%
 
1904581.4%
 
1404381.4%
 
Other values (377)2357072.9%
 
(Missing)20146.2%
 
ValueCountFrequency (%) 
402< 0.1%
 
653< 0.1%
 
67170.1%
 
70310.1%
 
738< 0.1%
 
ValueCountFrequency (%) 
8081< 0.1%
 
7972< 0.1%
 
7881< 0.1%
 
7591< 0.1%
 
7554< 0.1%
 

Displacement
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)0.2%
Missing2147
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean3.542469091
Minimum0.65
Maximum7.5
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:20.746923image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.65
5-th percentile1.7
Q12.4
median3.5
Q34.7
95-th percentile6
Maximum7.5
Range6.85
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.412911972
Coefficient of variation (CV)0.3988494847
Kurtosis-0.9847698785
Mean3.542469091
Median Absolute Deviation (MAD)1.1
Skewness0.4136593619
Sum106872.75
Variance1.996320239
MonotocityNot monotonic
2020-12-30T03:39:20.974644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2378611.7%
 
2.522527.0%
 
3.521446.6%
 
319436.0%
 
3.617185.3%
 
614264.4%
 
2.414164.4%
 
412673.9%
 
5.711283.5%
 
1.811123.4%
 
Other values (50)1197737.1%
 
(Missing)21476.6%
 
ValueCountFrequency (%) 
0.657< 0.1%
 
1590.2%
 
1.2300.1%
 
1.3600.2%
 
1.42850.9%
 
ValueCountFrequency (%) 
7.52< 0.1%
 
7230.1%
 
6.8300.1%
 
6.7514< 0.1%
 
6.7190.1%
 

Trans Description Cont.
Categorical

MISSING

Distinct3
Distinct (%)< 0.1%
Missing2037
Missing (%)6.3%
Memory size252.5 KiB
Automatic
19086 
Manual
9597 
CVT
 
1596
ValueCountFrequency (%) 
Automatic1908659.1%
 
Manual959729.7%
 
CVT15964.9%
 
(Missing)20376.3%
 
2020-12-30T03:39:21.188839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:21.341628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:21.447239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length7.434552544
Min length3

Trans Type
Categorical

MISSING

Distinct9
Distinct (%)< 0.1%
Missing1981
Missing (%)6.1%
Memory size252.5 KiB
6
11449 
5
7459 
4
4663 
8
3015 
<3
1668 
Other values (4)
2081 
ValueCountFrequency (%) 
61144935.4%
 
5745923.1%
 
4466314.4%
 
830159.3%
 
<316685.2%
 
713794.3%
 
94871.5%
 
101930.6%
 
CVT220.1%
 
(Missing)19816.1%
 
2020-12-30T03:39:21.661712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:21.818292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:21.977654image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.181550935
Min length1
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
MacPherson Strut
12220 
Double Wishbone
6358 
Independent
5051 
Coil Spring
2463 
Others
2440 
Other values (5)
3784 
ValueCountFrequency (%) 
MacPherson Strut1222037.8%
 
Double Wishbone635819.7%
 
Independent505115.6%
 
Coil Spring24637.6%
 
Others24407.6%
 
Link type17785.5%
 
Torsion Bar16625.1%
 
Axle1830.6%
 
Leaf type1130.3%
 
Air Suspension480.1%
 
2020-12-30T03:39:22.175356image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:22.335478image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:22.513991image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length15
Mean length13.14794529
Min length4
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
Link type
17788 
Leaf type
3020 
Others
2595 
Independent
2535 
Double Wishbone
2490 
Other values (5)
3888 
ValueCountFrequency (%) 
Link type1778855.0%
 
Leaf type30209.3%
 
Others25958.0%
 
Independent25357.8%
 
Double Wishbone24907.7%
 
Torsion Bar18535.7%
 
Coil Spring7182.2%
 
MacPherson Strut6782.1%
 
Axle5771.8%
 
Air Suspension620.2%
 
2020-12-30T03:39:22.706721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:22.878954image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:23.059330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length16
Median length9
Mean length9.604592152
Min length4
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
31407 
0
 
909
ValueCountFrequency (%) 
13140797.2%
 
09092.8%
 
2020-12-30T03:39:23.242070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
30785 
0
 
1531
ValueCountFrequency (%) 
13078595.3%
 
015314.7%
 
2020-12-30T03:39:23.289143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
28898 
1
3418 
ValueCountFrequency (%) 
02889889.4%
 
1341810.6%
 
2020-12-30T03:39:23.336712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
22443 
0
9873 
ValueCountFrequency (%) 
12244369.4%
 
0987330.6%
 
2020-12-30T03:39:23.384434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
30545 
1
 
1771
ValueCountFrequency (%) 
03054594.5%
 
117715.5%
 
2020-12-30T03:39:23.432334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
20695 
0
11621 
ValueCountFrequency (%) 
12069564.0%
 
01162136.0%
 
2020-12-30T03:39:23.478908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
17788 
0
14528 
ValueCountFrequency (%) 
11778855.0%
 
01452845.0%
 
2020-12-30T03:39:23.526249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Brakes-ABS
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
29657 
0
 
2659
ValueCountFrequency (%) 
12965791.8%
 
026598.2%
 
2020-12-30T03:39:23.574045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
18973 
0
13343 
ValueCountFrequency (%) 
11897358.7%
 
01334341.3%
 
2020-12-30T03:39:23.621680image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
17985 
0
14331 
ValueCountFrequency (%) 
11798555.7%
 
01433144.3%
 
2020-12-30T03:39:23.672102image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Traction Control
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
22922 
0
9394 
ValueCountFrequency (%) 
12292270.9%
 
0939429.1%
 
2020-12-30T03:39:23.719542image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
32272 
1
 
44
ValueCountFrequency (%) 
03227299.9%
 
1440.1%
 
2020-12-30T03:39:23.767207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
31130 
1
 
1186
ValueCountFrequency (%) 
03113096.3%
 
111863.7%
 
2020-12-30T03:39:23.814128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Fog Lamps
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
16616 
1
15700 
ValueCountFrequency (%) 
01661651.4%
 
11570048.6%
 
2020-12-30T03:39:23.861362image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
26438 
1
5878 
ValueCountFrequency (%) 
02643881.8%
 
1587818.2%
 
2020-12-30T03:39:23.908237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
22234 
0
10082 
ValueCountFrequency (%) 
12223468.8%
 
01008231.2%
 
2020-12-30T03:39:23.955306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
0
22051 
1
10265 
ValueCountFrequency (%) 
02205168.2%
 
11026531.8%
 
2020-12-30T03:39:24.003174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Stability Control
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
1
21730 
0
10586 
ValueCountFrequency (%) 
12173067.2%
 
01058632.8%
 
2020-12-30T03:39:24.051647image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Basic Miles/km
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing2117
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean42739.96709
Minimum24000
Maximum150000
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:24.125201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24000
5-th percentile36000
Q136000
median36000
Q350000
95-th percentile60000
Maximum150000
Range126000
Interquartile range (IQR)14000

Descriptive statistics

Standard deviation14332.19883
Coefficient of variation (CV)0.335334812
Kurtosis29.24604144
Mean42739.96709
Median Absolute Deviation (MAD)0
Skewness4.591368443
Sum1290704266
Variance205411923.2
MonotocityNot monotonic
2020-12-30T03:39:24.328690image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
360001986261.5%
 
50000715222.1%
 
6000022787.0%
 
1500002900.9%
 
240002550.8%
 
1000002140.7%
 
720001370.4%
 
497116< 0.1%
 
420005< 0.1%
 
(Missing)21176.6%
 
ValueCountFrequency (%) 
240002550.8%
 
360001986261.5%
 
420005< 0.1%
 
497116< 0.1%
 
50000715222.1%
 
ValueCountFrequency (%) 
1500002900.9%
 
1000002140.7%
 
720001370.4%
 
6000022787.0%
 
50000715222.1%
 

Basic Years
Real number (ℝ≥0)

MISSING

Distinct5
Distinct (%)< 0.1%
Missing2117
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean3.398754926
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:24.535997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q13
median3
Q34
95-th percentile5
Maximum6
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6622025812
Coefficient of variation (CV)0.194836814
Kurtosis1.151892098
Mean3.398754926
Median Absolute Deviation (MAD)0
Skewness1.308795495
Sum102639
Variance0.4385122586
MonotocityNot monotonic
2020-12-30T03:39:24.749714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
32018662.5%
 
4729422.6%
 
523017.1%
 
22770.9%
 
61410.4%
 
(Missing)21176.6%
 
ValueCountFrequency (%) 
22770.9%
 
32018662.5%
 
4729422.6%
 
523017.1%
 
61410.4%
 
ValueCountFrequency (%) 
61410.4%
 
523017.1%
 
4729422.6%
 
32018662.5%
 
22770.9%
 

Corrosion Miles/km
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing3004
Missing (%)9.3%
Memory size252.5 KiB
150000
20800 
100000
7401 
50000
 
1105
60000
 
6
ValueCountFrequency (%) 
1500002080064.4%
 
100000740122.9%
 
5000011053.4%
 
600006< 0.1%
 
(Missing)30049.3%
 
2020-12-30T03:39:24.990157image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:25.128668image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:25.245882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length8
Median length8
Mean length7.500835499
Min length3

Corrosion Years
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing2966
Missing (%)9.2%
Infinite0
Infinite (%)0.0%
Mean6.341056218
Minimum2
Maximum12
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:25.428459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q36
95-th percentile12
Maximum12
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.460939029
Coefficient of variation (CV)0.3880960748
Kurtosis1.21763819
Mean6.341056218
Median Absolute Deviation (MAD)0
Skewness1.665725887
Sum186110
Variance6.056220902
MonotocityNot monotonic
2020-12-30T03:39:25.609833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
51549247.9%
 
6673420.8%
 
12427113.2%
 
713214.1%
 
49653.0%
 
102680.8%
 
32250.7%
 
8640.2%
 
210< 0.1%
 
(Missing)29669.2%
 
ValueCountFrequency (%) 
210< 0.1%
 
32250.7%
 
49653.0%
 
51549247.9%
 
6673420.8%
 
ValueCountFrequency (%) 
12427113.2%
 
102680.8%
 
8640.2%
 
713214.1%
 
6673420.8%
 

Drivetrain Miles/km
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)< 0.1%
Missing2788
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean66368.11955
Minimum24000
Maximum150000
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:25.768020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum24000
5-th percentile36000
Q150000
median60000
Q370000
95-th percentile100000
Maximum150000
Range126000
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation22712.74158
Coefficient of variation (CV)0.3422236721
Kurtosis1.079040939
Mean66368.11955
Median Absolute Deviation (MAD)10000
Skewness1.054366614
Sum1959717834
Variance515868630.3
MonotocityNot monotonic
2020-12-30T03:39:25.958163image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
600001277139.5%
 
100000651920.2%
 
50000492715.2%
 
3600029289.1%
 
7000015874.9%
 
1500004101.3%
 
240002270.7%
 
720001370.4%
 
8000010< 0.1%
 
621396< 0.1%
 
Other values (2)6< 0.1%
 
(Missing)27888.6%
 
ValueCountFrequency (%) 
240002270.7%
 
3600029289.1%
 
50000492715.2%
 
600001277139.5%
 
621396< 0.1%
 
ValueCountFrequency (%) 
1500004101.3%
 
1250001< 0.1%
 
1200005< 0.1%
 
100000651920.2%
 
8000010< 0.1%
 

Drivetrain Years
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)< 0.1%
Missing2788
Missing (%)8.6%
Infinite0
Infinite (%)0.0%
Mean5.070069087
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:26.174498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median5
Q35
95-th percentile10
Maximum20
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.815610493
Coefficient of variation (CV)0.3581036988
Kurtosis16.64826543
Mean5.070069087
Median Absolute Deviation (MAD)0
Skewness3.096149132
Sum149709
Variance3.296441462
MonotocityNot monotonic
2020-12-30T03:39:26.337712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
51717253.1%
 
4493115.3%
 
331689.8%
 
1021006.5%
 
616395.1%
 
22420.7%
 
71050.3%
 
201000.3%
 
8710.2%
 
(Missing)27888.6%
 
ValueCountFrequency (%) 
22420.7%
 
331689.8%
 
4493115.3%
 
51717253.1%
 
616395.1%
 
ValueCountFrequency (%) 
201000.3%
 
1021006.5%
 
8710.2%
 
71050.3%
 
616395.1%
 

Turning Diameter - Curb to Curb (ft)
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct264
Distinct (%)0.9%
Missing3431
Missing (%)10.6%
Infinite0
Infinite (%)0.0%
Mean39.76493169
Minimum20.5
Maximum93.6
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:26.555319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum20.5
5-th percentile34.1
Q136
median38.1
Q342.7
95-th percentile51.2
Maximum93.6
Range73.1
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation5.674009053
Coefficient of variation (CV)0.1426887665
Kurtosis8.423493364
Mean39.76493169
Median Absolute Deviation (MAD)2.6
Skewness1.796363723
Sum1148610.052
Variance32.19437874
MonotocityNot monotonic
2020-12-30T03:39:26.770245image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
35.413074.0%
 
37.412623.9%
 
34.811503.6%
 
35.89653.0%
 
398192.5%
 
38.77902.4%
 
36.17242.2%
 
36.76872.1%
 
37.76692.1%
 
36.46542.0%
 
Other values (254)1985861.4%
 
(Missing)343110.6%
 
ValueCountFrequency (%) 
20.53< 0.1%
 
21.6712< 0.1%
 
21.7514< 0.1%
 
22.814< 0.1%
 
28.7370.1%
 
ValueCountFrequency (%) 
93.6250.1%
 
81.411< 0.1%
 
72.63< 0.1%
 
61.6210.1%
 
59.64< 0.1%
 

Front Wheel Material
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing1983
Missing (%)6.1%
Memory size252.5 KiB
Aluminum
19974 
Steel
7384 
Alloy
2972 
Carbon Fibre
 
3
ValueCountFrequency (%) 
Aluminum1997461.8%
 
Steel738422.8%
 
Alloy29729.2%
 
Carbon Fibre3< 0.1%
 
(Missing)19836.1%
 
2020-12-30T03:39:26.992049image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:27.137902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:27.250483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length8
Mean length6.732176012
Min length3

Stabilizer Bar Diameter - Front (in)
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)0.5%
Missing21109
Missing (%)65.3%
Infinite0
Infinite (%)0.0%
Mean1.133912198
Minimum0.63
Maximum1.68
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:27.475670image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.63
5-th percentile0.77
Q10.94
median1.13
Q31.34
95-th percentile1.42
Maximum1.68
Range1.05
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.2202381306
Coefficient of variation (CV)0.1942285576
Kurtosis-1.198987983
Mean1.133912198
Median Absolute Deviation (MAD)0.21
Skewness-0.2501690014
Sum12707.754
Variance0.04850483416
MonotocityNot monotonic
2020-12-30T03:39:27.734757image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.4211413.5%
 
1.3411043.4%
 
0.918672.7%
 
1.16301.9%
 
1.185331.6%
 
1.34851.5%
 
0.834351.3%
 
1.313981.2%
 
1.413911.2%
 
0.943671.1%
 
Other values (50)485615.0%
 
(Missing)2110965.3%
 
ValueCountFrequency (%) 
0.63240.1%
 
0.662< 0.1%
 
0.671010.3%
 
0.68180.1%
 
0.712020.6%
 
ValueCountFrequency (%) 
1.685< 0.1%
 
1.4211413.5%
 
1.41710< 0.1%
 
1.413911.2%
 
1.42190.7%
 

Roadside Assistance Years
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)0.1%
Missing21027
Missing (%)65.1%
Infinite0
Infinite (%)0.0%
Mean4.558065373
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:28.880007image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median4
Q35
95-th percentile20
Maximum20
Range18
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.849054254
Coefficient of variation (CV)0.8444491114
Kurtosis11.23386166
Mean4.558065373
Median Absolute Deviation (MAD)1
Skewness3.485818585
Sum51456
Variance14.81521865
MonotocityNot monotonic
2020-12-30T03:39:29.054237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
3378811.7%
 
428969.0%
 
525988.0%
 
213094.1%
 
206181.9%
 
6700.2%
 
1210< 0.1%
 
(Missing)2102765.1%
 
ValueCountFrequency (%) 
213094.1%
 
3378811.7%
 
428969.0%
 
525988.0%
 
6700.2%
 
ValueCountFrequency (%) 
206181.9%
 
1210< 0.1%
 
6700.2%
 
525988.0%
 
428969.0%
 

Roadside Assistance Miles/km
Real number (ℝ≥0)

MISSING

Distinct7
Distinct (%)< 0.1%
Missing7023
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean79394.53604
Minimum25000
Maximum150000
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:29.235627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum25000
5-th percentile36000
Q136000
median60000
Q3100000
95-th percentile150000
Maximum150000
Range125000
Interquartile range (IQR)64000

Descriptive statistics

Standard deviation45007.96138
Coefficient of variation (CV)0.5668899098
Kurtosis-1.207621107
Mean79394.53604
Median Absolute Deviation (MAD)24000
Skewness0.6235172601
Sum2008126000
Variance2025716587
MonotocityNot monotonic
2020-12-30T03:39:29.428172image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%) 
36000728622.5%
 
150000616419.1%
 
60000463614.3%
 
100000360811.2%
 
5000025027.7%
 
700006612.0%
 
250004361.3%
 
(Missing)702321.7%
 
ValueCountFrequency (%) 
250004361.3%
 
36000728622.5%
 
5000025027.7%
 
60000463614.3%
 
700006612.0%
 
ValueCountFrequency (%) 
150000616419.1%
 
100000360811.2%
 
700006612.0%
 
60000463614.3%
 
5000025027.7%
 

Manufacturer
Categorical

Distinct43
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
Ford
4186 
Chevrolet
2977 
Toyota
2739 
Nissan
2238 
GMC
2066 
Other values (38)
18110 
ValueCountFrequency (%) 
Ford418613.0%
 
Chevrolet29779.2%
 
Toyota27398.5%
 
Nissan22386.9%
 
GMC20666.4%
 
Honda17465.4%
 
Volkswagen15654.8%
 
Subaru15044.7%
 
BMW12834.0%
 
Audi11833.7%
 
Other values (33)1082933.5%
 
2020-12-30T03:39:29.652720image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:29.870091image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length6
Mean length5.895964847
Min length3

Model year
Real number (ℝ≥0)

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.535493
Minimum1990
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:30.079653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1990
5-th percentile1995
Q12005
median2011
Q32015
95-th percentile2019
Maximum2019
Range29
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.198832339
Coefficient of variation (CV)0.003582336497
Kurtosis-0.2312915204
Mean2009.535493
Median Absolute Deviation (MAD)5
Skewness-0.7082445966
Sum64940149
Variance51.82318704
MonotocityNot monotonic
2020-12-30T03:39:30.298600image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
201828018.7%
 
201924317.5%
 
201622256.9%
 
201521036.5%
 
201318985.9%
 
201418975.9%
 
201218705.8%
 
201116715.2%
 
201014564.5%
 
200914374.4%
 
Other values (19)1252738.8%
 
ValueCountFrequency (%) 
19902350.7%
 
19912610.8%
 
19922860.9%
 
19932510.8%
 
19942980.9%
 
ValueCountFrequency (%) 
201924317.5%
 
201828018.7%
 
201622256.9%
 
201521036.5%
 
201418975.9%
 

Category
Categorical

MISSING

Distinct4
Distinct (%)< 0.1%
Missing1716
Missing (%)5.3%
Memory size252.5 KiB
Car
13829 
Pickup
7750 
SUV
7719 
Van
 
1302
ValueCountFrequency (%) 
Car1382942.8%
 
Pickup775024.0%
 
SUV771923.9%
 
Van13024.0%
 
(Missing)17165.3%
 
2020-12-30T03:39:30.526872image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:30.684404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:30.806990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length3.719457854
Min length3

Front tire width
Categorical

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size252.5 KiB
235
4257 
245
3951 
225
3594 
265
2703 
215
2515 
Other values (39)
15296 
ValueCountFrequency (%) 
235425713.2%
 
245395112.2%
 
225359411.1%
 
26527038.4%
 
21525157.8%
 
20525087.8%
 
25522136.8%
 
an19886.2%
 
T2417345.4%
 
27513894.3%
 
Other values (34)546416.9%
 
2020-12-30T03:39:31.035856image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique3 ?
Unique (%)< 0.1%
2020-12-30T03:39:31.280061image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.938420597
Min length2

Front tire aspect ratio
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)< 0.1%
Missing2012
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean59.16397175
Minimum30
Maximum85
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:31.471066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile40
Q150
median60
Q370
95-th percentile75
Maximum85
Range55
Interquartile range (IQR)20

Descriptive statistics

Standard deviation11.5608924
Coefficient of variation (CV)0.1954042647
Kurtosis-0.8884795187
Mean59.16397175
Median Absolute Deviation (MAD)10
Skewness-0.2853471568
Sum1792905
Variance133.6542331
MonotocityNot monotonic
2020-12-30T03:39:31.659294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
70533016.5%
 
65440113.6%
 
55432513.4%
 
60372011.5%
 
75358111.1%
 
45354711.0%
 
5023057.1%
 
4020946.5%
 
356902.1%
 
852080.6%
 
Other values (3)1030.3%
 
(Missing)20126.2%
 
ValueCountFrequency (%) 
30920.3%
 
356902.1%
 
4020946.5%
 
45354711.0%
 
5023057.1%
 
ValueCountFrequency (%) 
852080.6%
 
806< 0.1%
 
795< 0.1%
 
75358111.1%
 
70533016.5%
 
Distinct5
Distinct (%)< 0.1%
Missing6086
Missing (%)18.8%
Memory size252.5 KiB
R
16009 
H
5235 
V
2556 
T
 
1239
Z
 
1191
ValueCountFrequency (%) 
R1600949.5%
 
H523516.2%
 
V25567.9%
 
T12393.8%
 
Z11913.7%
 
(Missing)608618.8%
 
2020-12-30T03:39:31.890324image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-30T03:39:32.040311image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:32.163620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length1
Mean length1.376655527
Min length1

Front tire rim size
Real number (ℝ≥0)

MISSING

Distinct8
Distinct (%)< 0.1%
Missing4499
Missing (%)13.9%
Infinite0
Infinite (%)0.0%
Mean17.24247762
Minimum15
Maximum22
Zeros0
Zeros (%)0.0%
Memory size252.5 KiB
2020-12-30T03:39:32.335773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile15
Q116
median17
Q318
95-th percentile20
Maximum22
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.453866945
Coefficient of variation (CV)0.0843189115
Kurtosis0.003670605953
Mean17.24247762
Median Absolute Deviation (MAD)1
Skewness0.508696364
Sum479634
Variance2.113729093
MonotocityNot monotonic
2020-12-30T03:39:32.511081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%) 
17761223.6%
 
18645320.0%
 
16599818.6%
 
1530789.5%
 
2021886.8%
 
1921336.6%
 
221960.6%
 
211590.5%
 
(Missing)449913.9%
 
ValueCountFrequency (%) 
1530789.5%
 
16599818.6%
 
17761223.6%
 
18645320.0%
 
1921336.6%
 
ValueCountFrequency (%) 
221960.6%
 
211590.5%
 
2021886.8%
 
1921336.6%
 
18645320.0%
 

Interactions

2020-12-30T03:37:58.395418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:58.511864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:58.740427image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:58.846123image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:58.951254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.055836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.161167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.263396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.365234image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.481791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.584773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.685934image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.786523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.893302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:37:59.993859image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.101489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.202439image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.303106image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.407971image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.517081image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.641603image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.743545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.845596image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:00.948644image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.051438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.155279image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.260861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.367843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.474882image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.583712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.688447image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.794344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:01.900084image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.144903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.257398image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.362965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.473365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.581014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.685736image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.791354image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:02.897516image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.003394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.111652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.219374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.326043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.433367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.537616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.644761image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.750002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.853240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:03.960429image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.073004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.178983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.284255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.387691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.491687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.597541image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.708384image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.815127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:04.922844image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.028997image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.133835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.241115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.347784image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.454735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.562240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.675337image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.784247image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.891595image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:05.999545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:06.115477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2020-12-30T03:38:52.379209image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:52.491312image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:52.597374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:52.706066image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:52.814864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:52.918958image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.024288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.129056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.236213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.343684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.450223image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.554917image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.659791image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.764790image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.869807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:53.975548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.083113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.190130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.300932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.407032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.512756image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.617067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.725546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.835104image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:54.943838image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.050688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.162244image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.273334image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.382112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.488913image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.595259image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.701056image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.809877image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:55.919088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.025896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.138829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.247005image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.355344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.462016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.569513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.676577image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.783937image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.889830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:56.995193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.101722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.205459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.314319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.428037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.532781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.640755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.748165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.861014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:57.967035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.075100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.181919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.289006image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.404043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.531889image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.640725image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.746322image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.852896image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:58.958193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.065075image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.174569image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.281047image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.396381image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.520732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.628438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.735907image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.841568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:38:59.948413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.058788image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.169576image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.277811image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.385616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.493919image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.617890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.722632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.829379image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:00.935328image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.042430image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.147050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.257586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.366570image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.473317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.578353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.684476image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.790545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:01.896653image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:02.004652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:02.113203image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:02.219512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:03.020401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:03.125683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:03.232401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-30T03:39:32.770062image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-30T03:39:33.431485image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-30T03:39:34.102984image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-30T03:39:34.800246image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-30T03:39:36.603170image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-30T03:39:03.730930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:08.927254image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:11.238059image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-30T03:39:12.780527image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

MSRPEPA Fuel Economy Est - City (MPG)EngineDrivetrainPassenger CapacityPassenger DoorsBase Curb Weight (lbs)Passenger Volume (ft³)Wheelbase (in)Track Width, Front (in)Height, Overall (in)Fuel Tank Capacity, Approx (gal)SAE Net Torque @ RPMFuel SystemSAE Net Horsepower @ RPMDisplacementTrans Description Cont.Trans TypeSuspension Type - FrontSuspension Type - RearAir Bag-Frontal-DriverAir Bag-Frontal-PassengerAir Bag-Passenger Switch (On/Off)Air Bag-Side Body-FrontAir Bag-Side Body-RearAir Bag-Side Head-FrontAir Bag-Side Head-RearBrakes-ABSChild Safety Rear Door LocksDaytime Running LightsTraction ControlNight VisionRollover Protection BarsFog LampsParking AidTire Pressure MonitorBack-Up CameraStability ControlBasic Miles/kmBasic YearsCorrosion Miles/kmCorrosion YearsDrivetrain Miles/kmDrivetrain YearsTurning Diameter - Curb to Curb (ft)Front Wheel MaterialStabilizer Bar Diameter - Front (in)Roadside Assistance YearsRoadside Assistance Miles/kmManufacturerModel yearCategoryFront tire widthFront tire aspect ratioFront tire speed ratings/cons.typeFront tire rim size
040600.022.0l4FWD543790.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111000111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV23555.0H19.0
145500.022.0l4FWD543829.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111001111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV23555.0H19.0
243600.022.0l4FWD543821.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111001111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV25545.0V20.0
337400.022.0l4FWD543783.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111000011150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV23555.0H19.0
442600.021.0l4AWD544026.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111000111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV23555.0H19.0
547500.021.0l4AWD544068.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111001111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV23555.0H19.0
645600.021.0l4AWD544015.0104.0108.364.265.717.1280.0DI272.02.0Automatic10MacPherson StrutLink type11010111111001111150000.04.0150000.05.070000.06.039.0AluminumNaNNaN50000.0Acura2019SUV25545.0V20.0
737500.019.0V6AWD543902.0103.5105.763.165.016.0252.0SFI279.03.5Automatic6MacPherson StrutLink type11010111111000011150000.04.0150000.05.070000.06.038.9AluminumNaNNaN50000.0Acura2018SUV23560.0V18.0
841000.020.0V6FWD543772.0103.5105.763.165.016.0252.0SFI279.03.5Automatic6MacPherson StrutLink type11010111111000011150000.04.0150000.05.070000.06.038.9AluminumNaNNaN50000.0Acura2018SUV23560.0V18.0
939700.020.0V6FWD543768.0103.5105.763.165.016.0252.0SFI279.03.5Automatic6MacPherson StrutLink type11010111111000011150000.04.0150000.05.070000.06.038.9AluminumNaNNaN50000.0Acura2018SUV23560.0V18.0

Last rows

MSRPEPA Fuel Economy Est - City (MPG)EngineDrivetrainPassenger CapacityPassenger DoorsBase Curb Weight (lbs)Passenger Volume (ft³)Wheelbase (in)Track Width, Front (in)Height, Overall (in)Fuel Tank Capacity, Approx (gal)SAE Net Torque @ RPMFuel SystemSAE Net Horsepower @ RPMDisplacementTrans Description Cont.Trans TypeSuspension Type - FrontSuspension Type - RearAir Bag-Frontal-DriverAir Bag-Frontal-PassengerAir Bag-Passenger Switch (On/Off)Air Bag-Side Body-FrontAir Bag-Side Body-RearAir Bag-Side Head-FrontAir Bag-Side Head-RearBrakes-ABSChild Safety Rear Door LocksDaytime Running LightsTraction ControlNight VisionRollover Protection BarsFog LampsParking AidTire Pressure MonitorBack-Up CameraStability ControlBasic Miles/kmBasic YearsCorrosion Miles/kmCorrosion YearsDrivetrain Miles/kmDrivetrain YearsTurning Diameter - Curb to Curb (ft)Front Wheel MaterialStabilizer Bar Diameter - Front (in)Roadside Assistance YearsRoadside Assistance Miles/kmManufacturerModel yearCategoryFront tire widthFront tire aspect ratioFront tire speed ratings/cons.typeFront tire rim size
3230654100.022.0l4AWD544135.0NaN120.563.7NaNNaN295.0DI316.02.0Automatic8Double WishboneLink type11010111111001111150000.04.0150000.012.050000.04.038.7AluminumNaN4.0150000.0Volvo2018Car24545.0V18.0
3230758600.022.0l4AWD544135.0NaN120.563.7NaNNaN295.0DI316.02.0Automatic8Double WishboneLink type11010111111001111150000.04.0150000.012.050000.04.038.7AluminumNaN4.0150000.0Volvo2018Car25540.0V19.0
3230848100.024.0l4FWD543870.0NaN120.563.7NaNNaN258.0DI250.02.0Automatic8Double WishboneLink type11010111111001111150000.04.0150000.012.050000.04.038.7AluminumNaN4.0150000.0Volvo2018Car24545.0V18.0
3230934300.018.0I6RWD543461.090.3109.159.2NaNNaN199.0Electric FI181.02.9Automatic4MacPherson StrutLink type11010001110001000050000.04.0150000.08.0NaNNaN31.8Alloy0.91NaNNaNVolvo1998Car20555.0V16.0
3231041850.022.0l4AWD543920.093.0109.262.1NaNNaN258.0DI240.02.0Automatic8MacPherson StrutLink type11010111111000110150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2018Car23550.0V18.0
3231145700.022.0l4AWD543920.093.0109.262.1NaNNaN258.0DI240.02.0Automatic8MacPherson StrutLink type11010111111000111150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2018Car23550.0V18.0
3231241200.020.0l5AWD543602.093.0109.362.1NaNNaN266.0SFI250.02.5Automatic6MacPherson StrutLink type11010111111000110150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2016Car23550.0V18.0
3231344850.020.0l5AWD543602.093.0109.362.1NaNNaN266.0SFI250.02.5Automatic6MacPherson StrutLink type11010111111000111150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2016Car23550.0V18.0
3231441000.020.0l5AWD543602.093.0109.362.1NaNNaN266.0SFI250.02.5Automatic6MacPherson StrutLink type11010111111000110150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2015Car23550.0V18.0
3231544650.020.0l5AWD543602.093.0109.362.1NaNNaN266.0SFI250.02.5Automatic6MacPherson StrutLink type11010111111000111150000.04.0150000.012.050000.04.037.1AluminumNaN4.0150000.0Volvo2015Car23550.0V18.0